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Course Outline
Advanced TensorFlow Techniques
Building Deep Learning Models with TensorFlow
Collaborative Deep Learning Projects
Data Preprocessing for Deep Learning
Getting Started with TensorFlow
Introduction to Deep Learning
Introduction to Google Colab for Deep Learning
Optimizing Deep Learning Models
Summary and Next Steps
Tips and Best Practices
Understanding Neural Networks
- Creating neural network models
- Training neural networks
- Evaluating model performance
- Effective deep learning techniques
- Avoiding common pitfalls
- Enhancing model performance
- Hyperparameter tuning
- Regularization techniques
- Model optimization strategies
- Implementing convolutional neural networks (CNNs)
- Implementing recurrent neural networks (RNNs)
- Transfer learning with TensorFlow
- Introduction to neural networks
- Architecture of neural networks
- Activation functions and layers
- Overview of Google Colab
- Setting up Google Colab
- Navigating the Google Colab interface
- Overview of TensorFlow
- Setting up TensorFlow in Google Colab
- Basic TensorFlow operations
- Overview of deep learning
- Importance of deep learning
- Applications of deep learning
- Preparing datasets for training
- Data augmentation techniques
- Handling large datasets in Google Colab
- Sharing and collaborating on notebooks
- Real-time collaboration features
- Best practices for collaborative projects
Requirements
Audience
- Basic knowledge of machine learning
- Experience with Python programming
- Data scientists
- Software developers
14 Hours
Testimonials (2)
Organization, adhering to the proposed agenda, the trainer's vast knowledge in this subject
Ali Kattan - TWPI
Course - Natural Language Processing with TensorFlow
Very updated approach or CPI (tensor flow, era, learn) to do machine learning.
